Artificial neural network based fatigue life assessment of riveted joints in AA2024 aluminum alloy plates and optimization of riveted joints parameters-Reference-Cited by-同舟云学术

Artificial neural network based fatigue life assessment of riveted joints in AA2024 aluminum alloy plates and optimization of riveted joints parameters

Published:2024-01 Issue: Volume:178 Page:107997
ISSN:0142-1123
Container-title:International Journal of Fatigue
language:en
Short-container-title:International Journal of Fatigue

Author:

Masoudi Nejad Reza,Sina Nima,Ma Wenchen,Song Wei^ORCID,Zhu S.P.,Branco Ricardo^ORCID,Macek Wojciech^ORCID,Gholami Aboozar

Funder

Xi’an Jiaotong University

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering,Mechanics of Materials,General Materials Science,Modeling and Simulation

Reference31 articles.

1. Experimental and numerical investigation of fatigue crack growth behavior and optimizing fatigue life of riveted joints in Al-alloy 2024 plates;Nejad;Theor Appl Fract Mech,2020

2. Fatigue performance prediction of Al-alloy 2024 plates in riveted joint structure;Nejad;Eng Fail Anal,2021

3. Effect of load transfer by friction on the fatigue behaviour of riveted lap joints;Skorupa;Int J Fatigue,2016

4. Mohammadreza Tohidi, Wenchen Ma. On fatigue life prediction of Al-alloy 2024 plates in riveted joints;Nejad;Structures,2021

5. Fatigue and crack-growth analyses of riveted lap-joints in a retired aircraft;Newman;Int J Fatigue,2016

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1. Riveting damage behavior and mechanical performance investigation of CFRP/CFRP thin-walled single-lap blind riveted joints;Journal of Manufacturing Processes;2024-12

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3. Effect of the countersunk rivet head dimensions on fatigue behavior of riveted specimens of 2024-T3 alloy;Aircraft Engineering and Aerospace Technology;2024-08-27

4. Multi- objective modeling and optimization of dissimilar laser welding by integrating an artificial intelligence predictive model with NSGA-II algorithm;Materials Today Communications;2024-08

5. Machine learning-based fatigue life prediction of lamellar titanium alloys: A microstructural perspective;Engineering Fracture Mechanics;2024-06